Feature identification device, feature identification method, and computer readable medium

ABSTRACT

A similarity calculation unit (213) treats each of a plurality of classifications as a target classification, and calculates a similarity between a reference feature value, which is a feature value extracted from image data of the target classification, and a recognition feature value, which is a feature value extracted from recognition target data, which is image data to be recognized. An influence calculation unit (214) calculates an influence on the similarity with regard to each partial image of the recognition target data by taking as input the similarity with regard to each of the classifications and the recognition feature value. A feature identification unit (215) changes the recognition feature value in accordance with the influence.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of PCT International Application No.PCT/JP2019/046249, filed on Nov. 26, 2019, which claims priority under35 U.S.C. 119(a) to Patent Application No. 2019-056844, filed in Japanon Mar. 25, 2019, all of which are hereby expressly incorporated byreference into the present application.

TECHNICAL FIELD

The present invention relates to a technique to identify features ofimage data.

BACKGROUND ART

In financial institutions, government offices, or the like, processessuch as registration, change, and deletion of data using paper documentssuch as application forms are performed. The paper documents areprocessed, for example, in the flow of (1) sorting the documents, (2)checking for deficiencies, (3) (if there is no deficiency) registeringcontents, and (4) (if there is any deficiency) returning a document to aperson who has filled it in. Since labor costs are required forperforming these processes, it is desired to automate these processes.

As an effort to automate these processes, there is a system in whichpaper documents are digitized, entry items and entry contents arerecognized by a computer, and deficiencies are determined (for example,see Patent Literature 1). In such a system, initial settings such asdefining entry areas in paper documents are required before the systemis placed in operation. It takes time and effort to perform theseinitial settings manually.

As a method for automatically extracting an entry area from image dataof a paper document, the following method may be considered. A templateis printed out or filled in using a paper document in advance. Then, thetemplate is identified from image data of the paper document, and aportion other than the template is identified as an entry area. In orderto realize this method, a technique to identify a template included inimage data is required.

In a plurality of paper documents filled in by a plurality of persons,some entry areas may be filled in and some entry areas may not be filledin. In addition, the contents entered in the entry areas and the shapesof characters vary. Therefore, there is a high probability that featureportions in image data of the plurality of paper documents are atemplate. If there are paper documents of a plurality ofclassifications, there is a high probability that portions thatcontribute to the identification of each classification, that is,feature portions unique to that classification in the image data of thepaper documents of each classification are a template.

Patent Literature 2 describes a technique to extract features of imagedata using a convolutional neural network (CNN). Non-Patent Literature 1describes a technique to identify a portion in image data that serves asthe basis for classification by a CNN.

CITATION LIST Patent Literature

-   Patent Literature 1: JP 2018-067180 A-   Patent Literature 2: JP 2016-110232 A

Non-Patent Literature

-   Non-Patent Literature 1: R. Selvaraju et al, “Grad-CAM: Visual    Explanations from Deep Networks via Gradient-based Localization”

SUMMARY OF INVENTION Technical Problem

When a feature portion unique to each classification is to be identifiedusing the techniques described in Patent Literature 2 and Non-PatentLiterature 1, it is necessary to construct a CNN that has learnedparameters of each classification. Therefore, each time a classificationis added, parameters of the CNN need to be re-learned, requiringcomputer resources and time.

It is an object of the present invention to allow a feature portionunique to each classification to be appropriately identified withoutre-learning parameters of a CNN even when a classification is added.

Solution to Problem

A feature identification device according to the present inventionincludes

a similarity calculation unit to treat each of a plurality ofclassifications as a target classification, and calculate a similaritybetween a reference feature value and a recognition feature value, thereference feature value being a feature value extracted from image dataof the target classification, the recognition feature value being afeature value extracted from recognition target data, which is imagedata to be recognized;

an influence calculation unit to calculate an influence on thesimilarity with regard to each partial image of the recognition targetdata by taking as input the similarity with regard to each of theplurality of classifications calculated by the similarity calculationunit and the recognition feature value; and

a feature identification unit to change the recognition feature value inaccordance with the influence calculated by the influence calculationunit.

Advantageous Effects of Invention

In the present invention, a similarity between a reference feature valueand a recognition feature value is calculated with regard to eachclassification, and an influence on the similarity is calculated withregard to each partial image of recognition target data. Then, therecognition feature value is changed in accordance with the influence.

This allows a feature portion to be appropriately identified even whenthe reference feature value and the recognition feature value areextracted using a CNN that does not have information on classifications.Therefore, even when a classification is added, a feature portion can beappropriately identified without re-learning parameters of the CNN forextracting reference feature values and recognition feature values.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a configuration diagram of a feature identification system 100according to a first embodiment;

FIG. 2 is a hardware configuration diagram of a machine learning device10 and a feature identification device 20 according to the firstembodiment;

FIG. 3 is a functional configuration diagram of the machine learningdevice 10 according to the first embodiment;

FIG. 4 is a functional configuration diagram of the featureidentification device 20 according to the first embodiment;

FIG. 5 is a flowchart illustrating operation of the machine learningdevice 10 according to the first embodiment;

FIG. 6 is a flowchart illustrating operation of the featureidentification device 20 according to the first embodiment;

FIG. 7 is a flowchart of a similarity calculation process according tothe first embodiment;

FIG. 8 is a flowchart of an influence calculation process according tothe first embodiment;

FIG. 9 is a flowchart of a feature identification process according tothe first embodiment; and

FIG. 10 is a flowchart of a similarity calculation process according toa second variation.

DESCRIPTION OF EMBODIMENTS First Embodiment

***Description of Configurations***

Referring to FIG. 1 , a configuration of a feature identification system100 according to a first embodiment will be described.

The feature identification system 100 includes a machine learning device10 and a feature identification device 20. The machine learning device10 and the feature identification device 20 are connected via acommunication channel 30 such as a local area network (LAN), and canexchange data with each other.

The machine learning device 10 and the feature identification device 20may be realized by one device. In this case, the machine learning device10 and the feature identification device 20 can exchange data via asignal line or the like of the device.

Referring to FIG. 2 , hardware configurations of the machine learningdevice 10 and the feature identification device 20 according to thefirst embodiment will be described.

Each of the machine learning device 10 and the feature identificationdevice 20 is a computer.

The machine learning device 10 includes a processor 11, a memory 12, anauxiliary storage device 13, and a communication interface 14. Thefeature identification device 20 includes a processor 21, a memory 22,an auxiliary storage device 23, a communication interface 24, and anoutput interface 25.

Each of the processors 11 and 21 is an integrated circuit (IC) thatperforms processing. Specific examples of each of the processors 11 and21 are a central processing unit (CPU), a digital signal processor(DSP), and a graphics processing unit (GPU).

Each of the memories 12 and 22 is a storage device to temporarily storedata. Specific examples of each of the memories 12 and 22 are a staticrandom access memory (SRAM) and a dynamic random access memory (DRAM).

Each of the auxiliary storage devices 13 and 23 is a storage device tostore data. A specific example of each of the auxiliary storage devices13 and 23 is a hard disk drive (HDD). Each of the auxiliary storagedevices 13 and 23 may be a portable recording medium, such as a SecureDigital (SD, registered trademark) memory card, CompactFlash (CF,registered trademark), a NAND flash, a flexible disk, an optical disc, acompact disc, a Blu-ray (registered trademark) disc, or a digitalversatile disc (DVD).

Each of the communication interfaces 14 and 24 is an interface forcommunicating with external devices. Specific examples of each of thecommunication interfaces 14 and 24 are an Ethernet (registeredtrademark) port and a Universal Serial Bus (USB) port.

The output interface 25 is an interface for communicating with equipmentsuch as a display device. A specific example of the output interface 25is a High-Definition Multimedia Interface (HDMI, registered trademark)port.

Referring to FIG. 3 , a functional configuration of the machine learningdevice 10 according to the first embodiment will be described.

The machine learning device 10 includes, as functional components, asampling unit 111, a binary image conversion unit 112, an imagegeneration unit 113, a feature value extraction unit 114, an imagereconstruction unit 115, and a parameter updating unit 116. Thefunctions of the functional components of the machine learning device 10are realized by software.

The auxiliary storage device 13 stores programs for realizing thefunctions of the functional components of the machine learning device10. These programs are read by the processor 11 into the memory 12 andexecuted by the processor 11. This realizes the functions of thefunctional components of the machine learning device 10.

The auxiliary storage device 13 also stores pieces of learning imagedata 131, a first parameter 132, and a second parameter 133.

Referring to FIG. 4 , a functional configuration of the featureidentification device 20 according to the first embodiment will bedescribed.

The feature identification device 20 includes, as functional components,a feature extraction unit 211 and a feature changing unit 212. Thefeature changing unit 212 includes a similarity calculation unit 213, aninfluence calculation unit 214, and a feature identification unit 215.The functions of the functional components of the feature identificationdevice 20 are realized by software.

The auxiliary storage device 23 stores programs for realizing thefunctions of the functional components of the feature identificationdevice 20. These programs are read by the processor 21 into the memory22 and executed by the processor 21. This realizes the functions of thefunctional components of the feature identification device 20.

The auxiliary storage device 23 also stores one or more pieces ofregistered image data 231 for each of a plurality of classifications,one or more reference feature values 232 for each of the plurality ofclassifications, and one or more pieces of recognition target data 233.

FIG. 2 illustrates only one processor 11. However, a plurality ofprocessors 11 may be included, and the plurality of processors 11 mayexecute the programs for realizing the functions of the machine learningdevice 10 in cooperation. Similarly, a plurality of processors 21 may beincluded, and the plurality of processors 21 may execute the programsfor realizing the functions of the feature identification device 20 incooperation.

In the following description, exchange of data between the functionalcomponents within each of the devices may be performed by inter-processcommunication, or may be performed via the memory 12 or 22.

***Description of Operation***

Referring to FIGS. 5 to 9 , operation of the feature identificationsystem 100 according to the first embodiment will be described.

The operation of the feature identification system 100 according to thefirst embodiment corresponds to a feature identification methodaccording to the first embodiment. The operation of the featureidentification system 100 according to the first embodiment alsocorresponds to processes of a feature identification program accordingto the first embodiment.

Referring to FIG. 5 , operation of the machine learning device 10according to the first embodiment will be described.

The machine learning device 10 performs processes indicated in FIG. 5 bytreating each of the pieces of learning image data 131 stored in theauxiliary storage device 13 as target data. By this, the first parameter132 of a first neural network and the second parameter 133 of a secondneural network are updated.

Each of the pieces of learning image data 131 is image data of oneclassification of the plurality of classifications to be covered inlearning. In the first embodiment, each of the pieces of learning imagedata 131 is image data that is an image of a paper document of oneclassification of the plurality of classifications. Note thatinformation on the classification is not included in each of the piecesof learning image data 131.

(Step S11: Sampling Process)

The sampling unit 111 samples pixel data from the target learning imagedata 131, and converts the pixel data into image data of a referencesize. It is assumed here that each of the pieces of learning image data131 is grayscale.

(Step S12: Binary Image Conversion Process)

The binary image conversion unit 112 binarizes the grayscale learningimage data 131 that has been converted into the data of the referencesize in step S11 so as to generate converted binary data, which isbinary image data.

(Step S13: Image Generation Process)

The image generation unit 113 performs pre-processing necessary forlearning on the grayscale learning image data 131 that has beenconverted into the data of the reference size in step S11.

As a specific example, the image generation unit 113 removes noise atthe time of acquisition of the image from the learning image data 131 soas to cause the learning image data 131 to be in a smooth state.Alternatively, the image generation unit 113 adds noise to the learningimage data 131 so as to cause the learning image data 131 to havevariations. The following methods may be considered as methods foradding noise: adding Gaussian noise, salt-and-pepper noise, a Gaussianblur, or the like, performing operations such as rotation, shifting, andscaling, or adjusting setting values such as a brightness value,contrast, and sharpness.

(Step S14: Feature Value Extraction Process)

Using the first neural network, the feature value extraction unit 114extracts, as a learning feature value, a feature value of each pixelfrom the learning image data 131 on which the pre-processing has beenperformed in step S13. At this time, the first neural network extractsthe learning feature value by referring to the first parameter 132stored in the auxiliary storage device 13. In the first embodiment, thefirst neural network is assumed to be a CNN.

(Step S15: Image Reconstruction Process)

The image reconstruction unit 115 takes as input the learning featurevalue extracted in step S14, and using the second neural network,binarizes the learning image data 131 so as to generate learning binarydata, which is binary image data. At this time, the second neuralnetwork generates the learning binary data by referring to the secondparameter 133 stored in the auxiliary storage device 13. In the firstembodiment, the second neural network is assumed to be a CNN.

(Step S16: Parameter Updating Process)

The parameter updating unit 116 calculates a difference between theconverted binary data generated in step S12 and the learning binary datagenerated in step S15. The parameter updating unit 116 updates the firstparameter 132, which is the parameter of the first neural network, andthe second parameter 133, which is the parameter of the second neuralnetwork, based on the calculated difference.

Referring to FIG. 6 , operation of the feature identification device 20according to the first embodiment will be described.

As a precondition for processes indicated in FIG. 6 , the featureextraction unit 211 treats each of the pieces of registered image data231 stored in the auxiliary storage device 23 as target data, extracts afeature value of each pixel from the target registered image data 231 asa reference feature value 232, and stores it in the auxiliary storagedevice 23.

One or more pieces of registered image data 231 are stored in theauxiliary storage device 23 for each of the plurality of classificationsto be covered in the processes. Each of the pieces of registered imagedata 231 is associated with information indicating a classification. Thepieces of registered image data 231 may include the same image data asthe learning image data 131, or may include only different image data.

Specifically, the feature extraction unit 211 extracts the referencefeature value 232 from the target registered image data 231, using as afeature extraction model the first neural network generated by themachine learning device 10. At this time, the first neural networkextracts the reference feature value 232 by referring to the firstparameter 132. Then, the feature extraction unit 211 stores thereference feature value 232 extracted from each of the pieces of theregistered image data 231 in the auxiliary storage device 23 inassociation with information indicating a classification.

As a result, one or more reference feature values 232 are stored in theauxiliary storage device 23 for each of the classifications.

(Step S21: Feature Extraction Process)

The feature extraction unit 211 extracts, as a recognition featurevalue, a feature value of each pixel from recognition target data 233,which is image data to be recognized.

The recognition target data 233 is image data of one classification ofthe plurality of classifications to be covered in the processes. Therecognition target data 233 may be the same image data as the learningimage data 131 or may be different image data.

Specifically, the feature extraction unit 211 extracts the recognitionfeature value from the recognition target data 233, using as a featureextraction model the first neural network generated by the machinelearning device 10. At this time, the first neural network extracts therecognition feature value by referring to the first parameter 132.

(Step S22: Similarity Calculation Process)

The similarity calculation unit 213 treats each of the plurality ofclassifications as a target classification, and calculates a similaritybetween the reference feature value 232, which is a feature valueextracted from the image data of the target classification, and therecognition feature value extracted in step S21.

(Step S23: Influence Calculation Process)

The influence calculation unit 214 takes as input the similarity withregard to each of the classifications calculated in step S22 and therecognition feature value extracted in step S21, and calculates aninfluence on the similarity with regard to each partial image of therecognition target data.

(Step S24: Feature Identification Process)

The feature identification unit 215 changes the recognition featurevalue extracted in step S21 in accordance with the influence calculatedin step S23, and identifies a feature portion in the recognition targetdata 233. Then, the feature identification unit 215 identifies theidentified portion as a template in the paper document and other portionas entry areas.

Referring to FIG. 7 , a similarity calculation process (step S22 of FIG.6 ) according to the first embodiment will be described.

The processes from step S31 to step S36 are performed by treating eachof the classifications as a target classification.

In step S31, the similarity calculation unit 213 acquires one or morereference feature values 232 of the target classification from theauxiliary storage device 23. In step S32, the similarity calculationunit 213 acquires the recognition feature value extracted in step S21 ofFIG. 6 .

In step S33, the similarity calculation unit 213 advances the process tostep S34 if more than one reference feature value 232 has been retrievedin step S31, and advances the process to step S35 if one referencefeature value 232 has been retrieved in step S31. In step S34, thesimilarity calculation unit 213 treats the average of the referencefeature values 232 as the reference feature value 232 of the targetclassification. The similarity calculation unit 213 may set onereference feature value 232 of the target classification by any method,which is not limited to averaging.

In step S35, the similarity calculation unit 213 calculates adissimilarity between the reference feature value 232 and therecognition feature value acquired in step S32.

As a specific example, the similarity calculation unit 213 calculatesthe dissimilarity by calculating the distance between the referencefeature value 232 and the recognition feature value. Specifically, thesimilarity calculation unit 213 calculates the dissimilarity between thereference feature value 232 and the recognition target data 233, using amean squared error of each pixel. In this case, the similaritycalculation unit 213 calculates a squared error, which is the distancebetween the reference feature value 232 and the recognition targetfeature value, for each pixel, and calculates the average of squarederrors of all pixels as the dissimilarity. That is, the similaritycalculation unit 213 calculates the dissimilarity as indicated inFormula 1.dissimilarity_(j)=Σ_(k)(feature_(k)−basis_(jk))²  (Formula 1)

Note that basis_(jk) is the reference feature value 232 of a pixel k ofthe registered image data 231 of a classification j, feature_(k) is therecognition feature value of the pixel k of the recognition target data233, and dissimilarity_(j) is the dissimilarity with regard to theclassification j.

As another specific example, the similarity calculation unit 213calculates the dissimilarity between the reference feature value 232 andthe recognition target data 233 by weighting the average squared error.

In this case, the weight is determined for each classification such thatthe higher the influence of a pixel is, the larger its value becomes.Alternatively, the weight is determined for each classification suchthat the greater the variation (standard deviation), among theregistered image data 231, of feature values of a pixel is, the smallerits value becomes. Alternatively, the weight may be determined by acombination of these two.

That is, the similarity calculation unit 213 calculates thedissimilarity as indicated in Formula 2.dissimilarity_(j)=Σ_(k) w _(jk)(feature_(k)−basis_(jk))²  (Formula 2)

Note that w_(jk) is the weight for the pixel k of the registered imagedata 231 of the classification j. The weight w_(jk) is, for example,|basis_(jk)|²/σ_(jk), where σ_(jk) is the standard deviation with regardto the pixel k of the registered image data 231 of the classification j.

In step S36, the similarity calculation unit 213 converts thedissimilarity calculated in step S35 into a similarity. At this time,the similarity calculation unit 213 calculates the similarity such thatthe higher the dissimilarity is, the lower the similarity becomes, andthe lower the dissimilarity is, the higher the similarity becomes. As aspecific example, the similarity calculation unit 213 calculates thesimilarity by normalizing the dissimilarity in the range of 0 to 1 andsubtracting the normalized dissimilarity from 1.

In step S37, the similarity calculation unit 213 outputs a vector whoseelements are the similarities respectively calculated for theclassifications, as a similarity vector.

Referring to FIG. 8 , an influence calculation process (step S23 of FIG.6 ) according to the first embodiment will be described.

In step S41, the influence calculation unit 214 acquires the similarityvector output in step S37 of FIG. 7 . In step S42, the influencecalculation unit 214 acquires the recognition feature value extracted instep S21 of FIG. 6 .

In step S43, the influence calculation unit 214 takes as input thesimilarity vector acquired in step S41 and the recognition feature valueacquired in step S42, and calculates, as an influence, a magnitude ofimpact on the similarity when each pixel, which is each partial image,of the recognition target data 233 is changed. Specifically, theinfluence calculation unit 214 calculates the influence of each pixel byinputting the similarity vector and the recognition feature value toGrad-CAM. That is, the influence is a gradient. Grad-CAM is described inNon-Patent Literature 1.

In step S44, the influence calculation unit 214 outputs the influencecalculated in step S43. Note that a feature value has a plurality oflayers. Therefore, the influence calculation unit 214 may output theinfluence of each of the layers of the feature value, or may output theaverage of the influences of the layers of the feature value.

Referring to FIG. 9 , a feature identification process (step S24 of FIG.6 ) according to the first embodiment will be described.

In step S51, the feature identification unit 215 acquires the influenceoutput in step S44 of FIG. 8 . In step S52, the feature identificationunit 215 acquires the recognition feature value extracted in step S21 ofFIG. 6 .

In step S53, the feature identification unit 215 uses the influenceacquired in step S51 as the weight and weights the recognition featurevalue acquired in step S52 so as to change the recognition featurevalue. At this time, if the influence is output for each layer, thefeature identification unit 215 performs weighting on a per layer basis.In step S54, the feature identification unit 215 outputs the recognitionfeature value changed in step S53 to a display device or the like viathe output interface 25. With this recognition feature value, thefeature portion of the recognition target data 233 has been identified.

***Effects of First Embodiment***

As described above, the feature identification device 20 according tothe first embodiment calculates a similarity between the referencefeature value 232 and a recognition feature value with regard to eachclassification, and calculates an influence on the similarity withregard to each partial image (each pixel) of the recognition target data233. Then, the recognition feature value is changed in accordance withthe influence.

This allows a feature portion to be appropriately identified even whenthe reference feature value 232 and the recognition feature value areextracted using a CNN that does not have information on classifications.Therefore, even when a classification is added, a feature portion uniqueto the classification can be appropriately identified withoutre-learning parameters of the CNN for extracting reference featurevalues and recognition feature values. As a result, even when aclassification is added, a template and an entry portion can beappropriately identified without re-learning parameters of the CNN.

***Other Configurations***

<First Variation>

The machine learning device 10 is required to satisfy the condition thatlearning of the first neural network is performed without includinginformation on a classification in learning image data 131 that is givenas input to the feature value extraction unit 114. As long as thiscondition is satisfied, the configuration of the machine learning device10 is not limited to the configuration described in the firstembodiment, and may be a different configuration.

<Second Variation>

In the first embodiment, the similarity is calculated by integrating thereference feature values 232, and then comparing the integratedreference feature value 232 with the recognition feature value. However,the similarity to the recognition feature value may be calculated foreach of the reference feature values 232 and then the similarities ofthe reference feature values 232 may be integrated.

Referring to FIG. 10 , the similarity calculation process (step S22 ofFIG. 6 ) in this case will be described.

The processes from step S61 to step S66 are performed by treating eachof the classifications as a target classification.

The processes from step S61 to step S62 are the same as the processesfrom step S31 to step S32 of FIG. 7 . In step S63, the similaritycalculation unit 213 calculates a dissimilarity between each referencefeature value 232 acquired in step S61 and the recognition feature valueacquired in step S62.

In step S64, the similarity calculation unit 213 advances the process tostep S65 if more than one reference feature value 232 has been retrievedin step S61, and advances the process to step S66 if one referencefeature value 232 has been retrieved in step S61. In step S65, thesimilarity calculation unit 213 treats the average of thedissimilarities to the reference feature values 232 calculated in stepS63 as the dissimilarity with regard to the target classification. Notethat the similarity calculation unit 213 may determine one dissimilarityfor the target classification by any method, which is not limited toaveraging.

The processes from step S66 to step S67 are the same as the processesfrom step S36 to step S37 of FIG. 7 .

<Third Variation>

In the first embodiment, the functional components are realized bysoftware. However, as a third variation, the functional components maybe realized by hardware. With regard to the third variation, differencesfrom the first embodiment will be described.

Configurations of the machine learning device 10 and the featureidentification device 20 according to the third variation will bedescribed.

When the functional components are realized by hardware, the machinelearning device 10 includes an electronic circuit in place of theprocessor 11, the memory 12, and the auxiliary storage device 13. Theelectronic circuit is a dedicated circuit that realizes the functions ofthe functional components, the memory 12, and the auxiliary storagedevice 13. Similarly, when the functional components are realized byhardware, the feature identification device 20 includes an electroniccircuit in place of the processor 21, the memory 22, and the auxiliarystorage device 23. The electronic circuit is a dedicated circuit thatrealizes the functions of the functional components, the memory 22, andthe auxiliary storage device 23.

The electronic circuit is assumed to be a single circuit, a compositecircuit, a programmed processor, a parallel-programmed processor, alogic IC, a gate array (GA), an application specific integrated circuit(ASIC), or a field-programmable gate array (FPGA).

The functional components may be realized by one electronic circuit, orthe functional components may be distributed among and realized by aplurality of electronic circuits.

<Fourth Variation>

As a fourth variation, some of the functional components may be realizedby hardware, and the rest of the functional components may be realizedby software.

Each of the processors 11, 21, the memories 12, 22, the auxiliarystorage devices 13, 23, and the electronic circuit is referred to asprocessing circuitry. That is, the functions of the functionalcomponents are realized by the processing circuitry.

REFERENCE SIGNS LIST

10: machine learning device, 11: processor, 12: memory, 13: auxiliarystorage device, 14: communication interface, 111: sampling unit, 112:binary image conversion unit, 113: image generation unit, 114: featurevalue extraction unit, 115: image reconstruction unit, 116: parameterupdating unit, 131: learning image data, 132: first parameter, 133:second parameter, 20: feature identification device, 21: processor, 22:memory, 23: auxiliary storage device, 24: communication interface, 211:feature extraction unit, 212: feature changing unit, 213: similaritycalculation unit, 214: influence calculation unit, 215: featureidentification unit, 231: registered image data, 232: reference featurevalue, 233: recognition target data, 30: communication channel, 100:feature identification system.

The invention claimed is:
 1. A feature identification device comprising:processing circuitry to: treat each of a plurality of classifications asa target classification, and calculate a similarity between a referencefeature value and a recognition feature value, the reference featurevalue being a feature value extracted from image data of the targetclassification, the recognition feature value being a feature valueextracted from recognition target data, which is image data to berecognized; calculate an influence on the similarity with regard to eachpartial image of the recognition target data by taking as input thesimilarity calculated with regard to each of the plurality ofclassifications and the recognition feature value; and change therecognition feature value in accordance with the calculated influence.2. The feature identification device according to claim 1, wherein theprocessing circuitry calculates the similarity by calculating a distancebetween the reference feature value and the recognition feature value.3. The feature identification device according to claim 1, wherein withregard to each of the plurality of classifications, the processingcircuitry calculates, as the influence, a magnitude of impact on thesimilarity when each partial image of the recognition target data ischanged.
 4. The feature identification device according to claim 1,wherein the processing circuitry changes the recognition feature valueby using the influence as a weight and weighting the recognition featurevalue.
 5. The feature identification device according to claim 1,wherein the reference feature value and the recognition feature valueare extracted by a feature extraction model that does not haveinformation on the plurality of classifications.
 6. The featureidentification device according to claim 5, wherein the featureextraction model is a first neural network generated by a machinelearning device that updates parameters of the first neural network anda second neural network, based on a difference between converted binarydata and learning binary data, the converted binary data being binaryimage data converted from learning data, which is image data to belearned, the learning binary data being binary image data resulting fromconverting the learning data by the second neural network based on alearning feature value, which is a feature value of the learning dataextracted by the first neural network.
 7. A feature identificationmethod comprising: treating each of a plurality of classifications as atarget classification, and calculating a similarity between a referencefeature value and a recognition feature value, the reference featurevalue being a feature value extracted from image data of the targetclassification, the recognition feature value being a feature valueextracted from recognition target data, which is image data to berecognized; calculating an influence on the similarity with regard toeach partial image of the recognition target data by taking as input thesimilarity with regard to each of the plurality of classifications andthe recognition feature value; and changing the recognition featurevalue in accordance with the influence.
 8. A non-transitory computerreadable medium storing a feature identification program that causes acomputer to function as a feature identification device to execute: asimilarity calculation process of treating each of a plurality ofclassifications as a target classification, and calculating a similaritybetween a reference feature value and a recognition feature value, thereference feature value being a feature value extracted from image dataof the target classification, the recognition feature value being afeature value extracted from recognition target data, which is imagedata to be recognized; an influence calculation process of calculatingan influence on the similarity with regard to each partial image of therecognition target data by taking as input the similarity with regard toeach of the plurality of classifications calculated by the similaritycalculation process and the recognition feature value; and a featureidentification process of changing the recognition feature value inaccordance with the influence calculated by the influence calculationprocess.